from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 42.463328 |
| daal4py_KNeighborsClassifier | 0.0 | 3.0 | 5.018768 |
| KNeighborsClassifier_kd_tree | 0.0 | 6.0 | 22.926067 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 43.130476 |
| KMeans_tall | 0.0 | 1.0 | 41.719820 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 20.050662 |
| KMeans_short | 0.0 | 0.0 | 15.309606 |
| daal4py_KMeans_short | 0.0 | 0.0 | 7.423445 |
| LogisticRegression | 0.0 | 1.0 | 4.110969 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 34.665548 |
| Ridge | 0.0 | 0.0 | 25.199624 |
| daal4py_Ridge | 0.0 | 0.0 | 6.653672 |
| total | 0.0 | 30.0 | 28.740353 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.143 | 0.003 | 1000000 | 1000000 | 100 | -1 | 1 | 5.595 | NaN | 0.979 | 0.982 | 0.512 | 0.005 | 0.279 | 0.006 | See | See |
| 1 | KNeighborsClassifier | predict | 26.839 | 0.391 | 1000000 | 1000 | 100 | -1 | 1 | 0.000 | 0.027 | 0.979 | 0.982 | 1.743 | 0.037 | 15.399 | 0.396 | See | See |
| 2 | KNeighborsClassifier | predict | 0.183 | 0.014 | 1000000 | 1 | 100 | -1 | 1 | 0.004 | 0.000 | 0.979 | 0.982 | 0.098 | 0.001 | 1.864 | 0.145 | See | See |
| 3 | KNeighborsClassifier | fit | 0.142 | 0.001 | 1000000 | 1000000 | 100 | -1 | 5 | 5.645 | NaN | 0.979 | 0.982 | 0.506 | 0.004 | 0.280 | 0.003 | See | See |
| 4 | KNeighborsClassifier | predict | 34.787 | 0.000 | 1000000 | 1000 | 100 | -1 | 5 | 0.000 | 0.035 | 0.979 | 0.982 | 1.712 | 0.015 | 20.324 | 0.183 | See | See |
| 5 | KNeighborsClassifier | predict | 0.197 | 0.015 | 1000000 | 1 | 100 | -1 | 5 | 0.004 | 0.000 | 0.979 | 0.982 | 0.092 | 0.001 | 2.147 | 0.162 | See | See |
| 6 | KNeighborsClassifier | fit | 0.145 | 0.002 | 1000000 | 1000000 | 100 | -1 | 100 | 5.501 | NaN | 0.979 | 0.982 | 0.501 | 0.003 | 0.290 | 0.004 | See | See |
| 7 | KNeighborsClassifier | predict | 34.701 | 0.000 | 1000000 | 1000 | 100 | -1 | 100 | 0.000 | 0.035 | 0.979 | 0.982 | 1.776 | 0.011 | 19.539 | 0.121 | See | See |
| 8 | KNeighborsClassifier | predict | 0.195 | 0.016 | 1000000 | 1 | 100 | -1 | 100 | 0.004 | 0.000 | 0.979 | 0.982 | 0.094 | 0.002 | 2.067 | 0.174 | See | See |
| 9 | KNeighborsClassifier | fit | 0.143 | 0.001 | 1000000 | 1000000 | 100 | 1 | 1 | 5.594 | NaN | 0.979 | 0.982 | 0.518 | 0.005 | 0.276 | 0.004 | See | See |
| 10 | KNeighborsClassifier | predict | 13.993 | 0.028 | 1000000 | 1000 | 100 | 1 | 1 | 0.000 | 0.014 | 0.979 | 0.982 | 1.708 | 0.019 | 8.194 | 0.092 | See | See |
| 11 | KNeighborsClassifier | predict | 0.214 | 0.003 | 1000000 | 1 | 100 | 1 | 1 | 0.004 | 0.000 | 0.979 | 0.982 | 0.094 | 0.001 | 2.289 | 0.033 | See | See |
| 12 | KNeighborsClassifier | fit | 0.141 | 0.001 | 1000000 | 1000000 | 100 | 1 | 5 | 5.668 | NaN | 0.979 | 0.982 | 0.522 | 0.014 | 0.270 | 0.008 | See | See |
| 13 | KNeighborsClassifier | predict | 22.313 | 0.011 | 1000000 | 1000 | 100 | 1 | 5 | 0.000 | 0.022 | 0.979 | 0.982 | 1.728 | 0.038 | 12.909 | 0.285 | See | See |
| 14 | KNeighborsClassifier | predict | 0.216 | 0.003 | 1000000 | 1 | 100 | 1 | 5 | 0.004 | 0.000 | 0.979 | 0.982 | 0.096 | 0.001 | 2.249 | 0.040 | See | See |
| 15 | KNeighborsClassifier | fit | 0.154 | 0.002 | 1000000 | 1000000 | 100 | 1 | 100 | 5.193 | NaN | 0.979 | 0.982 | 0.520 | 0.003 | 0.296 | 0.005 | See | See |
| 16 | KNeighborsClassifier | predict | 22.229 | 0.081 | 1000000 | 1000 | 100 | 1 | 100 | 0.000 | 0.022 | 0.979 | 0.982 | 1.819 | 0.044 | 12.219 | 0.298 | See | See |
| 17 | KNeighborsClassifier | predict | 0.215 | 0.002 | 1000000 | 1 | 100 | 1 | 100 | 0.004 | 0.000 | 0.979 | 0.982 | 0.097 | 0.001 | 2.218 | 0.031 | See | See |
| 18 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | -1 | 1 | 0.282 | NaN | 0.979 | 0.982 | 0.101 | 0.002 | 0.559 | 0.016 | See | See |
| 19 | KNeighborsClassifier | predict | 23.569 | 0.096 | 1000000 | 1000 | 2 | -1 | 1 | 0.000 | 0.024 | 0.979 | 0.982 | 0.255 | 0.000 | 92.423 | 0.395 | See | See |
| 20 | KNeighborsClassifier | predict | 0.020 | 0.001 | 1000000 | 1 | 2 | -1 | 1 | 0.001 | 0.000 | 0.979 | 0.982 | 0.006 | 0.000 | 3.581 | 0.328 | See | See |
| 21 | KNeighborsClassifier | fit | 0.058 | 0.001 | 1000000 | 1000000 | 2 | -1 | 5 | 0.278 | NaN | 0.979 | 0.982 | 0.101 | 0.002 | 0.571 | 0.012 | See | See |
| 22 | KNeighborsClassifier | predict | 32.105 | 0.000 | 1000000 | 1000 | 2 | -1 | 5 | 0.000 | 0.032 | 0.979 | 0.982 | 0.258 | 0.001 | 124.673 | 0.391 | See | See |
| 23 | KNeighborsClassifier | predict | 0.029 | 0.001 | 1000000 | 1 | 2 | -1 | 5 | 0.001 | 0.000 | 0.979 | 0.982 | 0.005 | 0.000 | 5.453 | 0.423 | See | See |
| 24 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | -1 | 100 | 0.280 | NaN | 0.979 | 0.982 | 0.099 | 0.001 | 0.580 | 0.011 | See | See |
| 25 | KNeighborsClassifier | predict | 32.045 | 0.000 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.032 | 0.979 | 0.982 | 0.304 | 0.001 | 105.497 | 0.213 | See | See |
| 26 | KNeighborsClassifier | predict | 0.031 | 0.002 | 1000000 | 1 | 2 | -1 | 100 | 0.001 | 0.000 | 0.979 | 0.982 | 0.006 | 0.000 | 5.587 | 0.547 | See | See |
| 27 | KNeighborsClassifier | fit | 0.058 | 0.001 | 1000000 | 1000000 | 2 | 1 | 1 | 0.276 | NaN | 0.979 | 0.982 | 0.101 | 0.002 | 0.572 | 0.015 | See | See |
| 28 | KNeighborsClassifier | predict | 11.037 | 0.041 | 1000000 | 1000 | 2 | 1 | 1 | 0.000 | 0.011 | 0.979 | 0.982 | 0.256 | 0.001 | 43.107 | 0.229 | See | See |
| 29 | KNeighborsClassifier | predict | 0.015 | 0.001 | 1000000 | 1 | 2 | 1 | 1 | 0.001 | 0.000 | 0.979 | 0.982 | 0.006 | 0.000 | 2.634 | 0.254 | See | See |
| 30 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | 1 | 5 | 0.280 | NaN | 0.979 | 0.982 | 0.101 | 0.001 | 0.567 | 0.006 | See | See |
| 31 | KNeighborsClassifier | predict | 19.847 | 0.015 | 1000000 | 1000 | 2 | 1 | 5 | 0.000 | 0.020 | 0.979 | 0.982 | 0.258 | 0.001 | 76.835 | 0.294 | See | See |
| 32 | KNeighborsClassifier | predict | 0.024 | 0.001 | 1000000 | 1 | 2 | 1 | 5 | 0.001 | 0.000 | 0.979 | 0.982 | 0.005 | 0.000 | 4.448 | 0.371 | See | See |
| 33 | KNeighborsClassifier | fit | 0.057 | 0.001 | 1000000 | 1000000 | 2 | 1 | 100 | 0.278 | NaN | 0.979 | 0.982 | 0.101 | 0.003 | 0.572 | 0.019 | See | See |
| 34 | KNeighborsClassifier | predict | 19.929 | 0.033 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.020 | 0.979 | 0.982 | 0.305 | 0.003 | 65.378 | 0.644 | See | See |
| 35 | KNeighborsClassifier | predict | 0.025 | 0.001 | 1000000 | 1 | 2 | 1 | 100 | 0.001 | 0.000 | 0.979 | 0.982 | 0.005 | 0.001 | 4.699 | 0.492 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 3.173 | 0.070 | 1000000 | 1000000 | 10 | -1 | 1 | 0.025 | NaN | 0.981 | 0.989 | 0.706 | 0.017 | 4.495 | 0.145 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.460 | 0.005 | 1000000 | 1000 | 10 | -1 | 1 | 0.000 | 0.000 | 0.981 | 0.989 | 0.108 | 0.001 | 4.283 | 0.054 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.006 | 1000000 | 1 | 10 | -1 | 1 | 0.017 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 18.309 | 25.694 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 3.167 | 0.057 | 1000000 | 1000000 | 10 | -1 | 5 | 0.025 | NaN | 0.981 | 0.989 | 0.723 | 0.004 | 4.382 | 0.082 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.860 | 0.027 | 1000000 | 1000 | 10 | -1 | 5 | 0.000 | 0.001 | 0.981 | 0.989 | 0.197 | 0.002 | 4.359 | 0.145 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | -1 | 5 | 0.020 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 13.192 | 6.322 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 3.165 | 0.086 | 1000000 | 1000000 | 10 | -1 | 100 | 0.025 | NaN | 0.981 | 0.989 | 0.702 | 0.014 | 4.506 | 0.154 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.863 | 0.028 | 1000000 | 1000 | 10 | -1 | 100 | 0.000 | 0.003 | 0.981 | 0.989 | 0.599 | 0.004 | 4.781 | 0.055 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.008 | 0.001 | 1000000 | 1 | 10 | -1 | 100 | 0.011 | 0.000 | 0.981 | 0.989 | 0.001 | 0.000 | 11.386 | 4.835 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 3.199 | 0.078 | 1000000 | 1000000 | 10 | 1 | 1 | 0.025 | NaN | 0.981 | 0.989 | 0.737 | 0.008 | 4.343 | 0.116 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.778 | 0.009 | 1000000 | 1000 | 10 | 1 | 1 | 0.000 | 0.001 | 0.981 | 0.989 | 0.113 | 0.003 | 6.892 | 0.200 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | 1 | 1 | 0.063 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 4.880 | 2.556 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 3.223 | 0.073 | 1000000 | 1000000 | 10 | 1 | 5 | 0.025 | NaN | 0.981 | 0.989 | 0.730 | 0.030 | 4.414 | 0.207 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.510 | 0.008 | 1000000 | 1000 | 10 | 1 | 5 | 0.000 | 0.002 | 0.981 | 0.989 | 0.209 | 0.003 | 7.226 | 0.121 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.001 | 1000000 | 1 | 10 | 1 | 5 | 0.038 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 6.835 | 3.322 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 3.080 | 0.050 | 1000000 | 1000000 | 10 | 1 | 100 | 0.026 | NaN | 0.981 | 0.989 | 0.758 | 0.024 | 4.063 | 0.147 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 5.140 | 0.043 | 1000000 | 1000 | 10 | 1 | 100 | 0.000 | 0.005 | 0.981 | 0.989 | 0.634 | 0.009 | 8.105 | 0.133 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.006 | 0.001 | 1000000 | 1 | 10 | 1 | 100 | 0.013 | 0.000 | 0.981 | 0.989 | 0.001 | 0.000 | 8.941 | 3.887 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 0.785 | 0.016 | 1000000 | 1000000 | 2 | -1 | 1 | 0.020 | NaN | 0.981 | 0.989 | 0.476 | 0.007 | 1.649 | 0.040 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.026 | 0.000 | 1000000 | 1000 | 2 | -1 | 1 | 0.001 | 0.000 | 0.981 | 0.989 | 0.001 | 0.000 | 33.859 | 12.469 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | -1 | 1 | 0.007 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 19.166 | 16.936 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 0.778 | 0.016 | 1000000 | 1000000 | 2 | -1 | 5 | 0.021 | NaN | 0.981 | 0.989 | 0.470 | 0.007 | 1.657 | 0.042 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.027 | 0.000 | 1000000 | 1000 | 2 | -1 | 5 | 0.001 | 0.000 | 0.981 | 0.989 | 0.001 | 0.000 | 23.997 | 7.289 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | -1 | 5 | 0.007 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 20.032 | 17.363 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 0.782 | 0.011 | 1000000 | 1000000 | 2 | -1 | 100 | 0.020 | NaN | 0.981 | 0.989 | 0.468 | 0.004 | 1.670 | 0.028 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.046 | 0.000 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.000 | 0.981 | 0.989 | 0.006 | 0.001 | 7.030 | 0.725 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | -1 | 100 | 0.007 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 17.504 | 13.365 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 0.763 | 0.011 | 1000000 | 1000000 | 2 | 1 | 1 | 0.021 | NaN | 0.981 | 0.989 | 0.468 | 0.005 | 1.630 | 0.030 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.024 | 0.000 | 1000000 | 1000 | 2 | 1 | 1 | 0.001 | 0.000 | 0.981 | 0.989 | 0.001 | 0.000 | 32.561 | 10.409 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 1 | 0.027 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 5.361 | 4.926 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 0.746 | 0.003 | 1000000 | 1000000 | 2 | 1 | 5 | 0.021 | NaN | 0.981 | 0.989 | 0.464 | 0.005 | 1.607 | 0.017 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.025 | 0.000 | 1000000 | 1000 | 2 | 1 | 5 | 0.001 | 0.000 | 0.981 | 0.989 | 0.001 | 0.000 | 23.494 | 7.475 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 5 | 0.027 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 5.296 | 4.908 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 0.759 | 0.009 | 1000000 | 1000000 | 2 | 1 | 100 | 0.021 | NaN | 0.981 | 0.989 | 0.469 | 0.012 | 1.619 | 0.045 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.055 | 0.000 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.000 | 0.981 | 0.989 | 0.007 | 0.001 | 8.186 | 0.812 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 100 | 0.026 | 0.000 | 0.981 | 0.989 | 0.000 | 0.000 | 5.132 | 4.293 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.580 | 0.005 | 1000000 | 1000000 | 2 | k-means++ | 30 | 0.827 | NaN | 0.002 | 30 | 0.002 | 0.404 | 0.027 | 1.435 | 0.096 | See | See |
| 1 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 2 | k-means++ | 30 | 0.012 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 8.578 | 6.343 | See | See |
| 2 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | k-means++ | 30 | 0.013 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 10.842 | 8.611 | See | See |
| 3 | KMeans_tall | fit | 0.514 | 0.006 | 1000000 | 1000000 | 2 | random | 30 | 0.933 | NaN | 0.002 | 30 | 0.002 | 0.366 | 0.023 | 1.407 | 0.091 | See | See |
| 4 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1000 | 2 | random | 30 | 0.013 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 9.188 | 6.836 | See | See |
| 5 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | random | 30 | 0.013 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 10.266 | 8.052 | See | See |
| 6 | KMeans_tall | fit | 6.270 | 0.114 | 1000000 | 1000000 | 100 | k-means++ | 30 | 3.828 | NaN | 0.002 | 30 | 0.002 | 3.200 | 0.017 | 1.959 | 0.037 | See | See |
| 7 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | k-means++ | 30 | 0.514 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 5.729 | 3.040 | See | See |
| 8 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 100 | k-means++ | 30 | 0.645 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 10.149 | 7.759 | See | See |
| 9 | KMeans_tall | fit | 5.858 | 0.039 | 1000000 | 1000000 | 100 | random | 30 | 4.097 | NaN | 0.002 | 30 | 0.002 | 3.120 | 0.074 | 1.877 | 0.046 | See | See |
| 10 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | random | 30 | 0.512 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 5.683 | 3.166 | See | See |
| 11 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 100 | random | 30 | 0.647 | 0.0 | 0.002 | 30 | 0.002 | 0.000 | 0.000 | 10.056 | 7.540 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.218 | 0.002 | 10000 | 10000 | 2 | k-means++ | 20 | 0.015 | NaN | 0.303 | 20 | 0.318 | 0.084 | 0.001 | 2.584 | 0.052 | See | See |
| 1 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | k-means++ | 20 | 0.010 | 0.0 | 0.303 | 20 | 0.318 | 0.001 | 0.000 | 2.690 | 0.634 | See | See |
| 2 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 2 | k-means++ | 20 | 0.013 | 0.0 | 0.303 | 20 | 0.318 | 0.000 | 0.000 | 9.236 | 7.285 | See | See |
| 3 | KMeans_short | fit | 0.074 | 0.002 | 10000 | 10000 | 2 | random | 20 | 0.043 | NaN | 0.303 | 20 | 0.318 | 0.029 | 0.000 | 2.557 | 0.090 | See | See |
| 4 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | random | 20 | 0.010 | 0.0 | 0.303 | 20 | 0.318 | 0.001 | 0.000 | 2.632 | 0.589 | See | See |
| 5 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 2 | random | 20 | 0.013 | 0.0 | 0.303 | 20 | 0.318 | 0.000 | 0.000 | 7.402 | 4.288 | See | See |
| 6 | KMeans_short | fit | 0.597 | 0.016 | 10000 | 10000 | 100 | k-means++ | 20 | 0.268 | NaN | 0.303 | 20 | 0.318 | 0.318 | 0.003 | 1.878 | 0.053 | See | See |
| 7 | KMeans_short | predict | 0.003 | 0.000 | 10000 | 1000 | 100 | k-means++ | 20 | 0.319 | 0.0 | 0.303 | 20 | 0.318 | 0.001 | 0.000 | 1.979 | 0.605 | See | See |
| 8 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 100 | k-means++ | 20 | 0.594 | 0.0 | 0.303 | 20 | 0.318 | 0.000 | 0.000 | 8.169 | 4.983 | See | See |
| 9 | KMeans_short | fit | 0.203 | 0.002 | 10000 | 10000 | 100 | random | 20 | 0.789 | NaN | 0.303 | 20 | 0.318 | 0.131 | 0.002 | 1.550 | 0.029 | See | See |
| 10 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 100 | random | 20 | 0.337 | 0.0 | 0.303 | 20 | 0.318 | 0.001 | 0.000 | 2.146 | 0.389 | See | See |
| 11 | KMeans_short | predict | 0.001 | 0.000 | 10000 | 1 | 100 | random | 20 | 0.601 | 0.0 | 0.303 | 20 | 0.318 | 0.000 | 0.000 | 7.987 | 4.647 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | class_weight | l1_ratio | n_jobs | random_state | n_iter | throughput | latency | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 11.452 | 0.041 | 1000000 | 1000000 | 100 | NaN | NaN | NaN | NaN | [20] | [-0.10302895] | NaN | 0.24 | 2.123 | 0.024 | 5.395 | 0.063 | See | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | NaN | NaN | [20] | 2.4825862096241766 | 0.0 | 0.24 | 0.000 | 0.000 | 0.902 | 0.526 | See | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | NaN | NaN | [20] | 10.891599986003278 | 0.0 | 0.24 | 0.000 | 0.000 | 0.418 | 0.402 | See | See |
| 3 | LogisticRegression | fit | 0.824 | 0.007 | 1000 | 1000 | 10000 | NaN | NaN | NaN | NaN | [27] | [-2.59056809] | NaN | 0.24 | 0.849 | 0.032 | 0.971 | 0.038 | See | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | NaN | NaN | NaN | NaN | [27] | 4.531691848893503 | 0.0 | 0.24 | 0.003 | 0.000 | 0.583 | 0.120 | See | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | NaN | NaN | [27] | 77.90331028750583 | 0.0 | 0.24 | 0.001 | 0.000 | 0.140 | 0.100 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | max_iter | random_state | throughput | latency | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 0.183 | 0.001 | 1000 | 1000 | 10000 | NaN | NaN | 0.437 | NaN | 1.0 | 0.190 | 0.002 | 0.962 | 0.014 | See | See |
| 1 | Ridge | predict | 0.013 | 0.000 | 1000 | 1000 | 10000 | NaN | NaN | 6.158 | 0.0 | 1.0 | 0.021 | 0.001 | 0.616 | 0.032 | See | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | 1171.114 | 0.0 | 1.0 | 0.000 | 0.000 | 0.633 | 0.697 | See | See |
| 3 | Ridge | fit | 1.214 | 0.073 | 1000000 | 1000000 | 100 | NaN | NaN | 0.659 | NaN | 1.0 | 0.240 | 0.005 | 5.051 | 0.323 | See | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | 5.085 | 0.0 | 1.0 | 0.000 | 0.000 | 0.669 | 0.495 | See | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | 12.426 | 0.0 | 1.0 | 0.000 | 0.000 | 0.664 | 0.722 | See | See |
{
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"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
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{
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